| The LNAI series reports state-of-the-art results in artificial intelligence re-search， development， and education， at a high level and in both printed and electronic form. Enjoying tight cooperation with the R&D community， with numerous individuals， as well as with prestigious organizations and societies，LNAI has grown into the most comprehensive artificial intelligence research forum available. The scope of LNAI spans the whole range of artificial intelligence and intelli-gent information processing including interdisciplinary topics in a variety of application fields. The type of material published traditionally includes. —proceedings （published in time for the respective conference） —post-proceedings （consisting of thoroughly revised final full papers） —research monographs（which may be based on PhD work）. |
| Keynote Papers Decision Making with Uncertainty and Data Mining Complex Networks and Networked Data Mining In-Depth Data Mining and Its Application in Stock Market Relevance of Counting in Data Mining Tasks Invited Papers Term Graph Model for Text Classification A Latent Usage Approach for Clustering Web Transaction and Building User Profile Association Rules Mining Quantitative Association Rules on Overlapped Intervals An Approach to Mining Local Causal Relationships from Databases Mining Least Relational Patterns from Multi Relational Tables Finding All Frequent Patterns Starting from the Closure Multiagent Association Rules Mining in Cooperative Learning Systems VisAR: A New Technique for Visualizing Mined Association Rules An Efficient Algorithm for Mining Both Closed and Maximal Frequent Free Subtrees Using Canonical Forms Classification E-CIDIM: Ensemble of CIDIM Classifiers PartiMly Supervised Classification - Based on Weighted Unlabeled Samples Support Vector Machine Mining Correlated Rules for Associative Classification A Comprehensively Sized Decision Tree Generation Method for Interactive Data Mining of Very Large Databases Using Latent Class Models for Neighbors Selection in Collaborative Filtering A Polynomial Smooth Support Vector Machine for Classification Reducts in Incomplete Decision Tables Learning k-Nearest Neighbor Naive Bayes for Ranking One Dependence Augmented Naive Bayes Clustering A Genetic k-Modes Algorithm for Clustering Categorical Data …… Novel Algorithms Text Mining Multimedia Mining Sequential Data Mining and Time Series Mining Web Mining Biomedical Mining Advanced Applications Security and Privacy Issues Spatial Data Mining Streaming Data Mining Author Index |
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